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基于机器学习利用前列腺特异抗原密度(PSAD)鉴别前列腺癌与良性前列腺增生:中国单中心回顾性研究

Differentiating prostate cancer from benign prostatic hyperplasia using PSAD based on machine learning: Single-center retrospective study in China.

作者信息

Zhang Yiyan, Li Qin, Xin Yi, Lv Weiqi

出版信息

IEEE/ACM Trans Comput Biol Bioinform. 2018 Apr 3. doi: 10.1109/TCBB.2018.2822675.

DOI:10.1109/TCBB.2018.2822675
PMID:29993659
Abstract

The incidence of prostate cancer increases annually. Prostate cancer is an underreported and emerging problem in China. We conducted a cross-sectional study of 392 eligible patients from 710 men with prostate cancer or benign prostatic hyperplasia between 2000 and 2003. For total prostate-specific antigen, age, three diameters of prostate, prostate volume and prostate-specific antigen density seven indices, analysis of variance and t test were used to analyze the difference between the groups. A decision tree with pruning was established using the prostate-specific antigen density, age and transversal diameter of the prostate to screen the patient with prostate cancer. According to the established decision tree model, prostate-specific antigen density was the most important factor affecting the occurrence of prostate cancer. In elderly people over the age of 83 years, the transverse diameter of prostate cancer was smaller than that of benign prostatic hyperplasia, with prostate-specific antigen density less than . No additional index was introduced, and the detection rate of prostate cancer was 86.6 %.The specificity was enhanced to 78.1%.

摘要

前列腺癌的发病率逐年上升。在中国,前列腺癌是一个报告不足且正在出现的问题。我们对2000年至2003年间710名患有前列腺癌或良性前列腺增生的男性中的392名符合条件的患者进行了横断面研究。对于总前列腺特异性抗原、年龄、前列腺的三个直径、前列腺体积和前列腺特异性抗原密度这七个指标,采用方差分析和t检验来分析组间差异。使用前列腺特异性抗原密度、年龄和前列腺横径建立了带剪枝的决策树,以筛选前列腺癌患者。根据建立的决策树模型,前列腺特异性抗原密度是影响前列腺癌发生的最重要因素。在83岁以上的老年人中,前列腺癌的横径小于良性前列腺增生,前列腺特异性抗原密度小于 。未引入其他指标,前列腺癌的检出率为86.6%。特异性提高到78.1%。

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